Yong Tang, Yanhao Huang, Hongzhi Wang, et al. Framework for artificial intelligence analysis in large-scale power grids based on digital simulation[J]. CSEE Journal of Power and Energy Systems, 2018, 4(4): 459-468.
DOI:
Yong Tang, Yanhao Huang, Hongzhi Wang, et al. Framework for artificial intelligence analysis in large-scale power grids based on digital simulation[J]. CSEE Journal of Power and Energy Systems, 2018, 4(4): 459-468. DOI: 10.17775/CSEEJPES.2018.01010.
Framework for artificial intelligence analysis in large-scale power grids based on digital simulation
control and scientific research of power systems all require a variety of simulation analysis. Thus power grid simulation analysis is a fundamental supporting technology of large-scale power grids. In power grid simulation analysis
in addition to simulation calculations
there are many links for analysis and decision-making
relying on specialists. The introduction of advanced artificial intelligence technology provides a new method to improve the efficiency and accuracy of power grid simulation analysis. Nevertheless
the research of the related artificial intelligence technologies face a great deal of new challenges due to the complexity of the large-scale power grid simulation data
including massive volumes
high dimensionality
strong coupling and complex correlations. Also a great deal of knowledge and experience need to be integrated in the process of analysis. In order to deal with these challenges
based on the existing works
this paper focuses on the core scientific problem of artificial intelligence analysis and decision making related to the massive simulation results of large-scale power grids
and proposes an artificial intelligence analysis method framework for large-scale power grids based on digital simulation
which includes the power grid simulation analysis knowledge model with application method
the power grid simulation knowledge mining method and the artificial intelligence models with transfer learning ability of diversified grids as well as analyzing and calculation adjusting for large-scale power grid simulation results
etc. This work is expected to open up a new technical approach for large-scale power grid simulation analysis and provide strong technical support for the safe and stable operation of large-scale power grids.
Data-Sequence Modeling Based Causal Evaluation Method for Power Systems and Spatiotemporal Causality Variation Patterns
Spatiotemporal Data Graph Modeling and Exploration of Application Scenarios in “Power Grid One Graph”
Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control
Modeling and Stability Issues of Voltage-source Converter-dominated Power Systems: A Review
Graph Computing and Its Application in Power Grid Analysis
Related Author
Qi Chen
Gang Mu
Hongbo Liu
Changgang Wang
Peng Li
Zhen Dai
Yachen Tang
Guangyi Liu
Related Institution
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University)
China Southern Power Grid Digital Grid Research Institute Co., Ltd.
Univers, Santa Clara
School of Electrical Engineering and Automation, Wuhan University
State Key Laboratory for Management and Control of Complex System, Institute of Automation, Chinese Academy of Sciences